PostgreSQL Index Performance Explained with Python (Real Benchmark Test)

In this video, we explore how much an index really improves performance in PostgreSQL, using Python to measure real execution times. We start by creating a large EMPLOYEES table and inserting millions of rows to simulate a realistic production-like dataset. Then, using Python and PostgreSQL, we run multiple queries on the salary column without any index and measure their average execution times. After that, we create an index on the salary column and run the exact same queries again, using the same Python script. The performance difference is dramatic and clearly shows why indexes are so important for query optimization. Source Code: https://github.com/turtlecode/postgresql-python-examples/blob/main/index-performance.py 0:00 – What We’re Going to Do 0:11 – PostgreSQL: Create Table & Insert Data 0:52 – Python Performance Test (Before Index) 2:38 – Free Courses & Next Steps We also demonstrate what happens when the index is dropped and recreated, and explain how buffer cache and index pages affect the first execution time. This video is perfect if you want to: 🔸 Understand PostgreSQL index performance in practice 🔸 Learn how to benchmark database queries with Python 🔸 See the real impact of indexes on large tables 🔸 Improve query performance with simple, real-world examples ✔️ Free Courses: https://www.youtube.com/@turtlecode/courses ✔️ Join Channel: https://www.youtube.com/channel/UChc8lo6K1HPM4_WDjsMSH6g/join If you want to learn more Python and database performance topics, check out the Courses section for free tutorials. And if you’d like to support the channel, don’t forget to join and subscribe so we can keep learning together 🚀